Systems and methods have been developed to recommend content to users of home entertainment systems based on ratings of the content from neighbors (e.g., other home systems in a cable TV system or other entertainment network). It will readily be appreciated that to recommend content to a particular user, selecting the neighborhood members is important, because if the wrong neighborhood is used, the recommendations may not be very useful to the particular person to whom they are made.
As understood herein, current methods of neighborhood selection are not as optimum as they might be. For example, in collaborative filtering (CF), opinions from users in the form of ratings of items are collected, and when the system is asked for a recommendation, the system identifies similar users based on, e.g., similarity of demographics to suggest the items these users have liked in the past. This method is based only on the judgments of the user neighborhood.
User similarity can be estimated using cosine-based similarity between two users, but as recognized herein, relying solely on this method, the number of neighbors typically must be defined in advance without any good way to know how many neighbors is optimal. Thus, the number of neighbors typically is fixed in advance without knowledge of the optimum number of neighbors that might benefit an individual user. Complicating the issue is the fact that the total number of ways of forming a reasonably sized neighborhood of one thousand other users is greater than the number of atoms in the known universe, so that, as recognized herein, at best a pseudo-optimal neighborhood feasibly can be defined. It is to this problem that the present invention is directed.